Out-of-pocket payments and catastrophic expenditures due to traffic injuries in Ouagadougou, Burkina Faso
Bibliographic record
Abstract
OBJECTIVE: To estimate the out-of-pocket expenditures linked to Road Traffic Injuries in Ouagadougou, Burkina Faso, as well as the prevalence of catastrophic expenditures among those out-of-pocket payments, and to identify the socio-economic determinants of catastrophic expenditures due to Road Traffic Injuries. METHODS: We surveyed every admission at the only trauma unit of Ouagadougou between January and July 2015 at the time of their admission, 7 days and 30 days later. We estimate a total amount of out-of-pocket expenditures paid by each patient. We considered an expense as catastrophic when it represented 10% at least of the annual global consumption of the patient's household. We used linear models to determine if socio-economic characteristics were associated to a greater or smaller ratio between out-of-pocket payment and global annual consumption. FINDINGS: We surveyed 1323 Road injury victims three times (admission, Days 7 and 30). They paid in average 46,547 FCFA (83.64 US dollars) for their care, which represent a catastrophic expenditure for 19% of them. Less than 5% of the sample was covered by a health insurance scheme. Household economic status is found to be the first determinant of catastrophic health expenditure occurrence, exhibiting a significant and negative on the ratio between road injury expenditures and global consumption. CONCLUSION: Our findings highlight the importance of developing health insurance schemes to protect poor households from the economic burden of road traffic injuries and improve equity in front of health shocks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".